Data-Driven 3D Placement of UAV Base Stations for Arbitrarily Distributed Crowds

In this paper, we consider an Unmanned Aerial Vehicle (UAV)-assisted cellular system which consists of multiple UAV base stations (BSs) cooperating the terrestrial BSs. In such a heterogeneous network, for cellular operators, the problem is how to determine the appropriate number, locations, and altitudes of UAV-BSs to improve the system sumrate as well as satisfy the demands of arbitrarily flash crowds on data rates. We propose a data-driven 3D placement of UAV-BSs for providing an effective placement result with a feasible computational cost. The proposed algorithm searches for the appropriate number, location, coverage, and altitude of each UAV-BS in the serving area with the maximized system sumrate in polynomial time so as to guarantee the minimum data rate requirement of UE. The simulation results show that the proposed approach can improve system sumrate in comparison with the case without UAV-BSs.Comment: 6 pages, 3 figures, accepted by 2019 IEEE Global Communications Conference: Wireless Communications (Globecom2019 WC

Noninvasive technique for measurement of heartbeat regularity in zebrafish (Danio rerio) embryos

<p>Abstract</p> <p>Background</p> <p>Zebrafish (<it>Danio rerio</it>), due to its optical accessibility and similarity to human, has emerged as model organism for cardiac research. Although various methods have been developed to assess cardiac functions in zebrafish embryos, there lacks a method to assess heartbeat regularity in blood vessels. Heartbeat regularity is an important parameter for cardiac function and is associated with cardiotoxicity in human being. Using stereomicroscope and digital video camera, we have developed a simple, noninvasive method to measure the heart rate and heartbeat regularity in peripheral blood vessels. Anesthetized embryos were mounted laterally in agarose on a slide and the caudal blood circulation of zebrafish embryo was video-recorded under stereomicroscope and the data was analyzed by custom-made software. The heart rate was determined by digital motion analysis and power spectral analysis through extraction of frequency characteristics of the cardiac rhythm. The heartbeat regularity, defined as the rhythmicity index, was determined by short-time Fourier Transform analysis.</p> <p>Results</p> <p>The heart rate measured by this noninvasive method in zebrafish embryos at 52 hour post-fertilization was similar to that determined by direct visual counting of ventricle beating (<it>p </it>> 0.05). In addition, the method was validated by a known cardiotoxic drug, terfenadine, which affects heartbeat regularity in humans and induces bradycardia and atrioventricular blockage in zebrafish. A significant decrease in heart rate was found by our method in treated embryos (<it>p </it>< 0.01). Moreover, there was a significant increase of the rhythmicity index (p < 0.01), which was supported by an increase in beat-to-beat interval variability (<it>p </it>< 0.01) of treated embryos as shown by Poincare plot.</p> <p>Conclusion</p> <p>The data support and validate this rapid, simple, noninvasive method, which includes video image analysis and frequency analysis. This method is capable of measuring the heart rate and heartbeat regularity simultaneously via the analysis of caudal blood flow in zebrafish embryos. With the advantages of rapid sample preparation procedures, automatic image analysis and data analysis, this method can potentially be applied to cardiotoxicity screening assay.</p

ANALYSIS OF KEY CONSIDERATIONS OF THE PUBLIC WHEN CHOOSING RECREATIONAL ACTIVITIES

ABSTRACT This study aims to investigate the key considerations of the public when choosing recreational activities, and concludes the key factors to be considered when choosing recreational activities, as well as the influence of various factors by means of literature review, expert interview, questionnaire survey, and Analytical Hierarchy Process (AHP). Through analysis, this study identified 12 influential factors for selecting recreational activities, among which the most importan

Evaluating and Inducing Personality in Pre-trained Language Models

Standardized and quantified evaluation of machine behaviors is a crux of understanding LLMs. In this study, we draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors. Originating as a philosophical quest for human behaviors, the study of personality delves into how individuals differ in thinking, feeling, and behaving. Toward building and understanding human-like social machines, we are motivated to ask: Can we assess machine behaviors by leveraging human psychometric tests in a principled and quantitative manner? If so, can we induce a specific personality in LLMs? To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors; MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories. By systematically evaluating LLMs with MPI, we provide the first piece of evidence demonstrating the efficacy of MPI in studying LLMs behaviors. We further devise a Personality Prompting (P^2) method to induce LLMs with specific personalities in a controllable way, capable of producing diverse and verifiable behaviors. We hope this work sheds light on future studies by adopting personality as the essential indicator for various downstream tasks, and could further motivate research into equally intriguing human-like machine behaviors.Comment: Accepted at NeurIPS 2023 (Spotlight

PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)

Kovacs Effect Studied Using The Distinguishable Particles Lattice Model Of Glass

Kovacs effect is a characteristic feature of glassy relaxation. It consists in a non-monotonic evolution of the volume (or enthalpy) of a glass after a succession of two abrupt temperatures changes. The second change is performed when the instantaneous value of the volume coincides with the equilibrium one at the final temperature. While this protocol might be expected to yield equilibrium dynamics right after the second temperature change, the volume instead rises and reaches a maximum, the so-called Kovacs hump, before dropping again to the final equilibrium value. Kovacs effect constitutes one of the hallmarks of aging in glasses. In this paper we reproduce all features of the Kovacs hump by means of the Distinguishable Particles Lattice Model (DPLM) which is a particle model of structural glasses.Comment: 4 pages, 2 figure